Method for improving healthcare performance statistics

ABSTRACT

A method of enhancing the quality of medical care reporting is disclosed. In general, statistical data regarding medical procedures is gathered and organized into desired categories (e.g. mortality, length of stay, etc.). The data is then analyzed to determine exceptional cases which the healthcare provider should afford further review to ensure the information was accurately and completely recorded at the time the care was provided. In using this data, healthcare professionals are able to make note of inconsistencies in the coding and description of cases that may result in an inaccurate reflection of the severity of a particular case. Using the exception report tools, healthcare providers can determine deficiencies in the file history notation process, and ultimately correct these deficiencies to arrive at a more accurate recording system.

RELATED APPLICATION

This application claims priority to U.S. Provisional Ser. No. 60/876,475filed Dec. 22, 2006, the contents of which are expressly incorporatedherein by reference.

BACKGROUND

In the healthcare industry, an increased focus on quality andperformance improvement has necessitated the development of tools thatcan accurately and meaningfully monitor this type of information. Todate, the data that has been used to measure hospital and physicianquality has been garnered from a variety of resources and manipulatedinto indicators that attempt to shed light on providers' performance. Inthe case of New York, a commonly used dataset to create these types ofindicators is the Statewide Planning and Research Cooperative System(SPARCS) database, which was developed in 1979 as a means of collectinghospital discharge information. SPARCS has since expanded its datacollection efforts, and currently collects patient-specific informationfor every hospital discharge, ambulatory surgery patient, and emergencydepartment admission in New York State.¹ However, since this informationwas never originally intended to measure provider performance, there wasnever a major incentive on the part of the providers to make sure thatthis information was highly accurate. This is not to say that effortswere not made to ensure data integrity, but rather that a sense ofurgency was lacking since the information providers were submitting wasnot expected to be used for quality reporting or reimbursement purposes.The result is that any initial reporting efforts that were conceivedusing this data may depict a lesser quality of care than was trulyprovided by the institution or physician in question. This is a problemfaced by states and providers around the country. ¹New York StateDepartment of Health website.http://www.health.state.ny.us/statistics/sparcs/operations/overview.htm

Now that this information is being widely used by a multitude ofentities to measure hospital and physician performance, the importanceof the accuracy of the data is paramount. As CMS and private payersbegin to consider payment methodologies that consider quality as one ofthe factors that play into provider reimbursement, hospitals andphysicians are at risk for lower payments if their data does notrepresent the true quality of care provided. Monitoring providerperformance without acknowledging the fact that these data issues existmay lead to unfair redistribution of provider reimbursements. Providers,therefore, require a means of ensuring their respective data is as cleanand accurate as possible before it is released into the public forum.Furthermore, providers need access to this information viaeasily-understood and timely reports in order to track their ownperformance and progress over time.

Prior data systems have merely analyzed the data and provided hospitalsand caregivers with numbers associated with the raw data without takinginto account the many other factors that can and/or do cause the data tobe inaccurate. In this increasingly competitive healthcare environmentin which the focus on measuring performance is only continuing toincrease, it is crucial that resources are made available not only toensure the integrity of publicly-available data, but also to helpproviders monitor their own performance through the availability ofmeaningful and actionable information. Through the creation of ExceptionReports, there has been developed a product that accomplishes both ofthese goals.

SUMMARY

The present invention, referred to throughout this document as“Exception Reports”, uses publicly-available healthcare data or ahospital's own data to create a series of reports that flag various“exceptions” in the delivery of care. These reports allow healthcareproviders (e.g. hospitals) to select predetermined threshold values toflag cases for further review. Additionally, this invention furtheraffords providers the necessary diagnostic feedback to allow them tobetter organize their clinical and/or administrative protocols to yieldnumbers that are not only more accurate, but also more favorably reflectprovider s' performance. Focusing solely on improving clinical protocolswill not be enough, as negative reported outcomes are not always theresult of inadequate clinical care. Incomplete documentation or codingcan also result in reported data that does not truly represent the levelof care provided by a hospital or physician. Therefore, using theException Reports to identify and address both clinical andadministrative issues will ensure that the vast majority of factors thatcould potentially lead to data inaccuracies are accounted for. Lastly,because the information provided via these reports is recent andactionable, it can quickly be reviewed and possibly corrected forquality reporting and pay-for-performance initiatives, further ensuringthe integrity of the publicly-available data.

The foundation of the Exception Reports is a computer system that hasthe necessary hardware to store and analyze the data, as well as theapposite algorithms to allow for the isolation of specific cases fromthe entire data set based on predetermined sensitivity levels. The datais collected directly from hospital clients on a monthly or quarterlybasis. Once the data is received, it is run through the 3M Core GroupingSoftware, which risk-adjusts the data as appropriate on behalf of eachhospital client. Specifically, it classifies the cases into variousAll-Patient Refined Diagnosis Related Groups (APR-DRGs), which is apatient classification system that groups similar types of patientstogether, accounting for severity of illness and risk of mortality. Theprimary reason for severity adjustment is to remove the long-standingand valid criticism that evaluative comparisons of two or more disparategroups based on observed data is often not an effective methodology dueto differences in case mix between the groups under study. By usingrisk-adjusted data, physician s' arguments that “my patients are sicker”are no longer valid.

The resulting data is entered into a web-based platform and compiledinto a package of electronic and hard copy reports. While new ExceptionReports continue to be developed, the existing set includes reportscovering the areas of:

-   -   Mortality;    -   High costs;    -   Long stays;    -   One day and ambulatory sensitive condition stays;    -   Admissions from nursing homes,;    -   ICU/CCU cases;    -   AHRQ Patient Safety Indicators; and    -   Hospital Quality—cases involving mortality and/or complications.

The level of detail provided is dependent on the type of report producedand the data available, but includes case-level detail by APR-DRG, aswell as information by physician, whenever possible.

The Exception Reports are run and distributed on a monthly or quarterlybasis to hospital clients. Currently, they are disseminatedelectronically, via email, but can also be made available online througha secure web connection, in the form of paper reports, or placed as afile on a disk or CD. Possible issues highlighted by the reports includethe following:

-   -   Documentation and/or coding did not fully reflect the complexity        and hence the expected outcomes, cost and quality of the case;    -   Individual physician performance issues;    -   Hospital practice performance issues; and/or    -   Community practice performance issues.        By using readily available, recent data and organizing it in        this fashion, providers have a means with which to reflect upon        and upgrade their quality of care provided to their patients, as        well as to improve upon their current methods of documentation        and coding. The reports themselves are used to help facilitate        ongoing discussions among hospital leaders, medical records        staff, quality staff and physicians. By narrowing the scope of        cases via the predetermined algorithms, providers can        concentrate specifically on cases that are true outliers, rather        than focusing valuable time and energy on less important issues.        Once the exceptions are identified on the reports, further        investigation into specific cases is required to determine if        clinical or administrative issues truly exist. Depending on the        source of the problem, corrective action can be taken as        appropriate. Administrative issues such as inadequate        documentation and/or coding can be addressed through additional        training and education of both the coding staff and physicians.        Clinical issues must be addressed in a manner conducive to the        situation.

This invention affords providers the benefit of not only increasing theaccuracy of their statistics, but also of representing the hospital aswell as the medical staff in the most favorable and fair way possible.Timely and organized access to this information is instrumental inensuring that issues with data integrity are corrected quickly and insuch a manner that minimizes any harm to the provider.

More importantly, our data services provide valuable filters that targetspecific cases as performance outliers. Hospital staff can focus onthese cases to improve performance and health outcomes. In a world wherehealthcare providers are bombarded by data and information, theexception reports organize this data so as to provide value in enhancingoperational decision-making and clinical performance. Without theseunique filters (exception reports), providers are relegated to acluttered data world; a world that is unorganized and not capable ofdriving change.

BRIEF DESCRIPTION OF DRAWING

FIG. 1 is a flow diagram showing the steps used to generate exceptionreports.

FIG. 2 is an exemplary Exception Report.

DETAILED DESCRIPTION

This description provides a detailed overview of the necessary stepsthat need to be taken from initial data collection to themonthly/quarterly distribution of Exception Reports for any client. Eachstage is broken into specific steps, which highlight the Specificactions executed. FIG. 1 shows these steps in the form of a flowchart.

-   -   I. Data Collection: a client's monthly or quarterly data is        collected through the necessary channels, such as SPARCS or from        client provided statistics, and saved to the appropriate        locations, and prepared for processing.    -   II. Data Validation and Initial Processing: A provider's data is        run through a series of manual and automated data validation        checks, while documenting any changes in the data file. Once the        file has been validated and checked for quality, a program is        run which appends the data file to the combined data file.    -   III. Data Grouping: Once the Combined data file has been checked        for quality, the data is run through 3M™ grouper software        creating a Grouper data Master file. After performing and        documenting a quality assurance check on the Grouper Master Data        file, the Grouper Master Data file is run through an SQL query,        which splits the Grouper Master Data file into manageable        datasets. Once these manageable datasets have been checked and        documented for quality, Exception Reports can be processed for        clients.    -   IV. Data Transfer to External Stage: At this point, the data is        transferred to External Stage (the offsite server). After the        data has been successfully transferred to External Stage, any        differences in the Internal Stage data to the External Stage        data is compared and documented to ensure data quality.    -   V. Production: Once the External Stage data is ready for        production, it is posted to Production, which is viewable by all        clients via on-line, web access. The Production data is reviewed        to ensure that the most recent data is viewable and Reports are        running correctly for each client.    -   VI. Exception Reports: After the data has been checked for        quality on Internal Stage, Exception Reports are run for all        clients who have submitted their data. Copies of the Exception        Reports are printed and checked for quality, then e-mailed to        clients. Follow-up analyses of Exception Report trends are        performed and may, for example, be e-mailed to each client        within 2 weeks of the initial reports distribution. Specific        filter levels (e.g. 5% or 10%) are designated for flagging        specific cases that fall outside a designated level for review,        such as mortality rate. It is noted that this filter for        denoting outlier cases may be set at any desired level, and the        5% filter used in the following example is merely a suggested        value.

FIG. 2 shows a Mortality Exception Report as an example of the reportsgenerated and disseminated to clients. The data contained in the reportis sorted by physician and includes patient specific demographicinformation and APR-DRG diagnostic information. Each case is assigned aseverity of illness and risk of mortality using the 3M grouper software.This report uses a statewide expected mortality benchmark to flag casesand prioritize for internal chart review. A filter of 5% is used toidentify outlying cases. That is, the data highlights for review thosecases where a patient has expired when there was a 95% chance ofsurvival based on acquired statistics.

While regrettable, very sick individuals admitted to hospitals arelikely to die while being treated. This report highlights that theexpected mortality rate for a number of patients, given their clinicalprofile, was above 39%. Other individuals in the report appear to not bevery sick (Risk of Mortality Levels 1-2) and yet expired. In terms ofpractical application, the mortality exception report allows hospitalstaff and physicians to examine case specific data for those mortalitiesthat were not expected or should not have occurred. Staff can prioritizethe cases for review and start the process of pulling charts to examinedocumentation/coding, operational issues, and/or clinical practiceissues. For example, upon pulling the charts of a case which shows up onthe exception report as a low mortality risk (e.g. femur fracture), thereviewer may determine that there were other mitigating factors, such asheart disease, blood disorder or cancer, that may have played a role inthe death of the patient but was not properly documented and/or coded inthe patient history. The failure to indicate all relevant informationpertinent to the specific case may result in an incorrectly identifiedRisk of Mortality level. This incorrect information may reflect poorlyon the healthcare provider making overall statistics appear as thoughthey are losing more patients to less severe conditions. Once issues areidentified, they can be incorporated into internal decision-making forcorrective action which may ultimately lead to a more accuraterepresentation of patient conditions from a given healthcare provider.

The Mortality Exception Report is an example of one of the exceptionreports. Other potential reports may include:

-   -   High Costs/Long Stays Exceptions;    -   One Day and Ambulatory Sensitive Condition Exceptions;    -   ICU/CCU Exceptions;    -   AHRQ Patient Safety Indicator Exceptions; and    -   Hospital Quality Exception cases.        Each report uses a specific benchmark to generate the exception.        Moreover, each report has practical application and is used by        clients to improve performance. Healthcare administrators are        able to use the exception reports to identify outlying cases,        and then determine if there is a need for more accurate clinical        annotations to properly identify and quantify risk factors so        that a more accurate representation of hospital and/or        healthcare provider statistics is made available to the public.

1. A method for improving the performance statistics of a healthcareprovider comprising: a) acquiring data related to the healthcareprovider's specific cases; b) organizing the data into specific categorygroupings; and c) identifying exceptional cases that fall within adesignated threshold level and presenting those exceptional cases to thehealthcare provider for further review.
 2. A method as in claim 1further comprising acquiring the data from a publicly accessibledatabase.
 3. A method as in claim 1 further comprising acquiring thedata directly from the healthcare provider.
 4. A method as in claim 1further comprising setting a threshold level according to an expectedmortality rate for a particular case.
 5. A method as in claim 1 furthercomprising setting a threshold level according to an expected cost of aparticular case.
 6. A method as in claim 1 further comprising settingthe threshold level according to an expected length of stay for aparticular case.
 7. A method as in claim 1 wherein the step oforganizing the data includes organizing data for each physiciancredentialed by the healthcare provider.
 8. A method as in claim 1further comprising providing feedback to the healthcare providerregarding possible performance and/or documentation issues within thehealthcare provider's network.